Re-imagining teacher assessment: How teachers are encouraged (or not) to pursue ongoing professional learning in New Brunswick
Bibliographic record
Abstract
Many conventional methods for evaluating teachers focus heavily on holding them accountable for their performance, often overlooking the importance of their personal and professional growth. This approach may have the unintended consequence of discouraging teachers from pursuing further development, as they may perceive that their efforts could be more valued or compensated more. As a result, it is crucial to adopt a more balanced approach that values both accountability and growth, which can help foster an environment that encourages teachers to be more engaged in their professional development. This paper re-imagines teacher assessment as a tool to foster ongoing learning. It explores how current assessment practices in New Brunswick, Canada, impact teachers’ engagement in professional development and learning. Using the case study approach, this paper incorporated semi-structured interviews and document reviews to investigate existing assessment frameworks, teacher perspectives, and the relationship between assessment and professional development participation. The key findings of this study highlight teachers’ willingness to enhance their professional expertise by participating in professional development and learning programs. However, teachers’ lack of participation in the selection and decision-making process of their preferred professional development and learning programs limits the effectiveness and relevance of meeting individual teacher’s needs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".